阅读说明：本技术 基于人工智能和大数据的药品包装设计方法 (Medicine package design method based on artificial intelligence and big data ) 是由 陈洁松 于 2021-10-15 设计创作，主要内容包括：本发明涉及一种基于人工智能和大数据的药品包装设计方法,包括：获得与目标药品同类产品的包装设计图,获得对应的设计分布图以及各区域风格特征；对设计分布图进行分析得到药品的最佳设计分布图；对各区域风格特征进行分析,构建拓扑图数据,根据拓扑图数据得到最佳的风格组合；根据药品的最佳分布图、最佳的风格组合以及自身内容得到最终的设计结果。(The invention relates to a medicine package design method based on artificial intelligence and big data, which comprises the following steps: obtaining a packaging design drawing of a target medicine like product, and obtaining a corresponding design distribution diagram and style characteristics of each region; analyzing the design distribution map to obtain an optimal design distribution map of the medicine; analyzing the style characteristics of each region, constructing topological graph data, and obtaining the optimal style combination according to the topological graph data; and obtaining a final design result according to the optimal distribution map, the optimal style combination and the content of the medicine.)

1. A medicine package design method based on artificial intelligence and big data is characterized by comprising the following steps:

step (ii) of: obtaining and targeting drugsPackaging design drawing of the same kind of product to obtain corresponding design distribution diagramAnd wind of each regionGrid character；

Step (ii) of: to design distribution diagramAnalyzing to obtain medicineBest design distribution map of；

Step (ii) of: for each region style characteristicAnalyzing, constructing topological graph data, and obtaining the optimal style combination according to the topological graph data;

step (ii) of: according to the medicineBest distribution map ofThe optimal style combination and the self content obtain the final design result;

wherein the stepsComprises the following steps: design drawing of packagingAfter reaching the same size, the data are sent into a semantic segmentation network which is divided intoThe structure comprises a package design chart of medicine input, a semantic segmentation image output, a design distribution chart of the package design chart obtained by the segmentation image with a character region pixel value of 1, a pattern region pixel value of 2, and a background region pixel value of 0；

Said step (c) isThe method for acquiring the style characteristics of the character area comprises the following steps: to obtainFirst, theFeature map of layerAnd the firstSaliency map of layer feature maps for character regionsFirst, the significance map is required to be alignedDown-sampling to size and feature mapsAre in agreement with to obtainSecond obtaining ofArea of middle pixel value 1When is coming into contact withStopping extracting style features of the current feature map to obtain style features of the character region; otherwise, extracting the current feature mapThe style characteristics of (1); will be provided withMask out feature mapIrrelevant area, only the character area is reserved, whereinThe result of the calculation is the multiplication of the pixel values of the corresponding positions; further reaction is obtainedThe Gramm matrix of style features of (1), wherein,the number of channels is(ii) a Then obtaining the gray matrix according to the calculation method of the Gramm matrix in the style migration technologyGramm matrix ofObtaining a characteristic diagramStyle characteristics of(ii) a Computing the style matrix starting from the first-level feature map untilObtaining the style characteristics of the character area of the package design drawing and recording the style characteristics asWhereinThe number of layers of the corresponding feature map; obtaining pattern region style characteristics according to the same methodAnd background region style characteristicsObtaining the design distribution map of each medicine package design drawingAnd style characteristics of each regionSaid style characteristicIncluding character region style featuresPattern area style characteristicsAnd background region style characteristics。

2. The artificial intelligence and big data based pharmaceutical packaging design method of claim 1, wherein said steps are as followsThe method comprises the following specific steps: calculating character region style characteristics of each medicineIs/are as followsDistance, considerThe distance is less than the threshold valueTwo character region style characteristics ofFor the same style, thresholdIs an empirical thresholdTo realizeAnClassifying the features; respectively completing the pattern region style characteristics according to the same methodAnd background region style characteristicsClassification of (1); each type of styleAnd the character region style characteristics are further obtained by respectively classifying the character regions into different categoriesPattern style characteristicsAnd background region style characteristicsScore of each subclass inThe scoring reflects the quality of the packaging effect of each style characteristic,product of the same kindA greater number of occurrences of the style feature in the design of (a) indicates a better effect of the style feature.

3. The method of claim 1, wherein the loss function of the semantic segmentation network is:

wherein the content of the first and second substances,a cross entropy loss function representing a semantic segmentation network;representing the number of classes of semantic segmentation;size information representing a design image of the pharmaceutical package;representation encoderThe number of layers of the middle feature map;is shown asCategories obtained in real time in a layer profileA saliency map of;is to show toCategories obtained in real time in a layer profileThresholding operation of the saliency map of (1);representing categories in label imagesA saliency map of;representing categories in label imagesThe thresholding operation of the saliency map of (1).

4. The method of claim 3, wherein the pair of second electrodesCategories obtained in real time in a layer profileIs to be the first oneCategories obtained in real time in a layer profileThe pixel point with the pixel value larger than 0 in the saliency image is set as 1.

5. The method of claim 3Wherein said pair of label images is classified intoThe thresholding operation of the saliency map of (1) is to label classes in the imageBelong to the category in the saliency imageThe pixel point of (1) is set to 1, and the other pixel points are set to 0.

6. The method of claim 1, wherein the drug is administered in accordance with a regimenBest distribution map ofThe step of obtaining the final design result by the optimal style combination and the self content comprises the following steps:

obtaining content information corresponding to the background area, the character area and the pattern area in the optimal distribution map according to the content of the user; obtaining style characteristics of a background area, style characteristics of a character area and style characteristics of a pattern area according to the optimal style combination; and obtaining a design result of the background area, the character area and the pattern area by using a style migration method according to the content information and the style characteristics.

Technical Field

The invention relates to the field of artificial intelligence, in particular to a medicine package design method based on artificial intelligence and big data.

Background

The design of medicine packaging must strictly follow the national order of medicine administration No. 24, and the packaging design is carried out according to the pharmacological characteristics of the medicine itself, so as to embody the functional characteristics of the medicine and explain to the consumer. Meanwhile, the cost is also emphasized in the design of the medicine package, the creative design is carried out on the medicine package design from the characteristics of the product, the expression modes of the like products are investigated, the pharmacological characteristics and the adaptive population classification of the product are positioned and analyzed according to the medicine management law and the relevant national policy rules, the commercial value of the medicine package is fully exerted, the medicine sale is promoted, the enterprise image is established, and the loyalty of consumers to enterprises or brands is enhanced. The uniqueness of the design of the drug package is closely related to the market competition.

In the product marketing business, in addition to the advertising role, it is important to distinguish the product from other products by the uniqueness of the product packaging design, and plays a very important role in "self-promotion" throughout the delivery. The existing packaging design of medicines needs designers to give design results according to experience, and the workload is large.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention adopts the following technical scheme:

a medicine package design method based on artificial intelligence and big data comprises the following steps:

step (ii) of: obtaining and targeting drugsPackaging design drawing of the same kind of product to obtain corresponding design distribution diagramAnd region style characteristics；

Step (ii) of: to design distribution diagramAnalyzing to obtain medicineBest design distribution map of；

Step (ii) of: for each region style characteristicAnalyzing, constructing topological graph data, and obtaining the optimal style combination according to the topological graph data;

step (ii) of: according to the medicineBest distribution map ofThe optimal style combination and the content of the style combination result in the final design result.

Further, the stepsComprises the following steps: obtaining market and target medicinePackaging design drawing of similar productsAfter reaching the same size, the data are sent into a semantic segmentation network which is divided intoThe structure comprises a package design chart of medicine as input, a semantic segmentation image as output, a character region with pixel value of 1, a pattern region with pixel value of 2, and a background region with pixel value of 0, and a package design obtained by the segmentation imageDesign distribution diagram of design chart。

Further, the stepsThe method for acquiring the style characteristics of the character area comprises the following steps: to obtainFirst, theFeature map of layerAnd the firstSaliency map of layer feature map for character region (c = 1)It should be noted that, in the following description,middle characteristic diagramIs obtained by down-sampling the input image, and the size and the saliency map thereofIs different in size, firstly, the significance map is required to be matchedDown-sampling to size and feature mapsAre in agreement with to obtainFirst, judgeArea of middle pixel value 1When is coming into contact withRepresenting the global feature of the character region acquired before the current feature map, stopping extracting the style feature of the current feature map, and obtaining the style feature of the character region; otherwise, executing step c) to extract the current feature mapThe style characteristics of (1); (ii) a

Will be provided withMask out feature mapIrrelevant area, only the character area is reserved, whereinThe result of the calculation is the multiplication of the pixel values of the corresponding positions; further reaction is obtainedThe Gramm matrix of stylistic features of (1), when required for explanation,is a multi-channel image with the number of channelsThe structure of (1) is determined by recording the number of channels as(ii) a Then the Gramm matrix can be obtained according to the calculation method in the style migration technologyGramm matrix of(ii) a Obtaining a characteristic diagramStyle characteristics of(ii) a Computing the style matrix starting from the first-level feature map untilObtaining the style characteristics of the character area of the package design drawing and recording the style characteristics asWhereinThe number of the corresponding feature map layers is, it should be noted that as the number of the feature map layers gradually increases, the receptive field of the feature map continuously increases, and the corresponding style features are representedGradually from the local style to the global style,obtaining the style characteristics of the pattern area for the global style characteristic matrix of the character area according to the same methodAnd background region style characteristicsObtaining the design distribution map of each medicine package design drawingAnd style characteristics of each regionStyle characteristicsThe device also comprises three parts: character region style characteristicsPattern style characteristicsAnd background region style characteristics。

Further, the stepsThe method comprises the following specific steps: calculating character region style characteristics of each medicineIs/are as followsDistance, considerThe distance is less than the threshold valueTwo character region style characteristics ofFor the same style, thresholdIs an empirical thresholdTo realizeAnClassifying the features; respectively completing the pattern region style characteristics according to the same methodAnd background region style characteristicsClassification of (1); each type of styleAnd the character region style characteristics are further obtained by respectively classifying the character regions into different categoriesPattern style characteristicsAnd background region style characteristicsScore of each subclass inThe scoring reflects the quality of the packaging effect of each style characteristic,design of individual productA greater number of occurrences of a stroke feature indicates a better effect of the style feature.

The invention has the beneficial effects that:

the design style of the different zones is different throughout the pharmaceutical product packaging design. The method analyzes the existing medicine package design result in the same product, and obtains the style characteristics of a character area, a pattern area and a background area; and obtaining the optimal medicine package design result according to the coordination of the style characteristics of each region and the distribution diagram of each region.

Detailed Description

The invention is described in detail below with reference to the figures and examples.

Step (ii) of: obtaining and targeting drugsPackaging design drawing of the same kind of product to obtain corresponding design distribution diagramAnd region style characteristics；

The purpose of the step is to analyze the design chart of the traditional Chinese medicine package of the same kind of products to obtain a design distribution chartAnd region style characteristics. The method has the beneficial effects that: extracting the characteristics of the existing similar medicine packages, and performing the subsequent stepsProvides the data.

Obtaining market and target medicinePackaging design drawing of similar productsAfter reaching the same size, the data are sent into a semantic segmentation network which is divided intoThe structure comprises a package design chart of medicine input, a semantic segmentation image output, a design distribution chart of the package design chart obtained by the segmentation image with a character region pixel value of 1, a pattern region pixel value of 2, and a background region pixel value of 0。

It should be noted that in the semantic division networkPerforming feature extraction on the input design drawing, determining the down-sampling times of one layer of feature drawing obtained by each down-sampling operationThe size of the output characteristic diagram and the size of the receptive field of a pixel point on the characteristic diagram on the input design diagram; to obtain each region style need to be pairedAnalyzing the extracted characteristic diagrams of each layer, and reflecting the style of each region in the design diagram by using the Gramm matrix of different layer characteristic diagrams, so whether the characteristics of each region can be extracted from each layer characteristic diagram or not directly influences the style characteristics of each regionThe accuracy of (2).

To ensureThe invention can extract the character area and pattern area from each layer of characteristic diagram, which improves the loss function of semantic segmentation network: the label of the semantic segmentation network is a category label of each pixel point in each medicine package design drawing, the pixel value of a character area is 1, the pixel value of a pattern area is 2, and the pixel value of a background area is 0 to obtain label data; to obtainThe significance map can reflect the characteristic of the convolutional neural network according to which classification is performed during classification, and it should be noted that one class corresponds to one CAM significance map, which is denoted byLayer feature map for categoriesIs shown inThen, the loss function of the semantic segmentation network in the invention is as follows:

wherein the content of the first and second substances,for the number of classes of semantic segmentation, the value in the invention isDesigning size information of the image for the pharmaceutical package;the cross entropy loss function of the traditional semantic segmentation network is not described herein again;for an encoderThe number of layers of the characteristic map in (2),is as followsCategories obtained in real time in a layer profileThe size of the saliency map of (a) is the same as the size of the pharmaceutical product package design image,for thresholding operations, the image is thresholdedSetting the numerical value of the pixel point with the middle pixel value larger than 0 as 1 to obtain a binary image;for belonging to a category in a label imageThe pixel point of (1) and the rest areas are binary images of 0.

It should be noted that the method for acquiring saliency maps of different categories is a known technique, and is not described herein again. And after the design of the loss function is finished, continuously and iteratively updating parameters in the model by using a gradient descent method to complete the training process. The output result of the network is the design distribution diagram. Further in pairAnalyzing the extracted characteristic graphs of all layers, and obtaining style characteristics of all regions from the characteristic graphs of all layersThe method for acquiring the style characteristics of the character area is introduced by taking the character area as an example:

a) to obtainFirst, theFeature map of layerAnd the firstSaliency map of layer feature map for character region (c = 1)It should be noted that, in the following description,middle characteristic diagramIs obtained by down-sampling the input image, and the size and the saliency map thereofIs different in size, firstly, the significance map is required to be matchedDown-sampling to size and feature mapsThe consistency is kept between the first and the second,to obtain；

b) First, it is judgedArea of middle pixel value 1Representing the global features of the character region acquired before the current feature map, stopping extracting the style features of the current feature map, and obtaining the style features of the character region; otherwise, executing step c) to extract the current feature mapThe style characteristics of (1);

c) will be provided withMask out feature mapIrrelevant area, only the character area is reserved, whereinThe result of the calculation is the multiplication of the pixel values of the corresponding positions; further reaction is obtainedThe Gramm matrix of stylistic features of (1), it should be noted that,is a multi-channel image with the number of channelsThe structure of (1) is determined by recording the number of channels as(ii) a Then the Gramm matrix can be obtained according to the calculation method in the style migration technologyGramm matrix of(ii) a The calculation method of the Gramm matrix is a known technology and is not described in detail herein;

d) thus, a characteristic diagram is obtainedStyle characteristics of(ii) a Computing the style matrix starting from the first-level feature map untilObtaining the style characteristics of the character area of the package design drawing and recording the style characteristics asWhereinThe number of layers of the corresponding feature map. As the number of feature layers gradually increases, the field of view of the feature map continuously increases, and the corresponding style features are representedGradually from the local style to the global style,is a global style feature matrix (Gramm matrix) of the character area.

Obtaining pattern region style characteristics according to the same methodAnd background region style characteristicsObtaining the design distribution map of each medicine package design drawingAnd style characteristics of each regionStyle characteristicsThe device also comprises three parts: character region style characteristicsPattern style characteristicsAnd background region style characteristics。

Step (ii) of: to design distribution diagramAnalyzing to obtain medicineBest design distribution map of；

The purpose of this step is to obtain a pharmaceutical productOptimum distribution design chart of. Firstly according to the stepsObtained by the method ofThe design distribution maps of the same kind of products need to be explained that all the design distribution maps are already designedTo the same size, willThe design distribution maps of the same size are stacked together to obtain a size ofOf three-dimensional data of (2), whereinThe drawing is first sized.

It should be noted that the pixel values in the design distribution map represent the categories of the corresponding pixel points, and therefore, the calculation is further performedIn the direction ofAverage pixel value for each location in the imageLocating the average pixel value atSetting the pixel value as 0, and using the pixel value as a background area; is located atSetting the pixel value as 1, and using the pixel value as a font area; is located atSetting the pixel value as 2 to be a pattern element area; to a size ofAn average design distribution map of; finally according to the target drugThe design size requirement of (a) averaging the design distribution mapObtaining an optimal design profile to a target dimension。

Step (ii) of: for each region style characteristicAnalyzing, constructing topological graph data, and obtaining the optimal style combination according to the topological graph data;

the purpose of the step is toIndividual region style characteristicsAnd analyzing to obtain the optimal combination of three different region styles. The method has the beneficial effects that: the visual effects of different styles and the matching among different styles can directly influence the effect of the packaging design, and the optimal visual effect is obtained according to a large amount of data analysisStyle and the best combination of styles.

Calculating character region style characteristics of each medicineIs/are as followsDistance, considerThe distance is less than the threshold valueTwo character region style characteristics ofFor the same style, thresholdIs an empirical thresholdTo realizeAnClassifying the features; respectively completing the pattern region style characteristics according to the same methodAnd background region style characteristicsClassification of (1); each type of styleAnd divided into different categories, each category of style being convenient for expressionCalled parent classes, and the different classes under each parent class are called child classes.

Further obtaining character region style characteristicsPattern style characteristicsAnd background region style characteristicsScore of each subclass inThe scoring reflects the quality of the packaging effect of each style characteristic,the more times of occurrence of the style characteristics in the design of the same type of products indicates that the style characteristics have better effect. Characterised by the character region styleObtaining each style feature for exampleScore of (a):

a) first, theThe character region style of the product is characterizedTherein compriseA style feature matrix (hereinafter Gramm matrix for convenience of description)Is marked asThe Gramm matrix on the different layer feature maps represents style features that do not pass size. Will be provided withGramm matrixes on the first-layer characteristic map of each product in the products are extracted to form a setA matrix of the number of Gramm,(ii) a Obtaining Gramm matrix set on each layer of characteristic diagram according to the same methodIt should be noted that the character region style characteristics of different products are recordedThe included Gramm matrixes are different, so the quantity of the matrixes in each Gramm matrix set is inconsistent;

b) for Gramm matrix setPerforming analysis and calculationBetween two of the Gramm matricesThe distance between the first and second electrodes,the distance calculation method is a known technology and is not described herein again; consider thatDistance between two adjacent platesLess than thresholdThe two Gramm matrices are the same matrix, thresholdIs an empirical threshold(ii) a Thus obtaining a setObtaining the score of each Gramm matrix according to the quantity of each Gramm matrix; product(s)In the collectionInner Gramm matrix；

c) Method pair according to step b)Analyzing the sets to obtain the score of each Gramm matrix, and obtaining the style characteristics of the character region according to the score of each Gramm matrixOf 1 atCharacter region style characteristics of individual productsThe score of (a) is:

obtaining character region style characteristics according to the same methodPattern style characteristicsAnd background region style characteristicsScore of either subclass. Regarding each seed class as a node, it should be noted that all nodes in the topology data are divided into three parent classes。

And further calculating to obtain the coordination coefficient of collocation between parents. Obtaining a parent classOne node of nextCalculate it with the other two parentsThe coordination coefficient of each node is calculated toThe calculation method is described as an example of the coordination coefficient: in thatIn a similar product, the style characteristics of a background area are firstly obtainedProducts of (1), as(ii) a Statistics ofThe style of the character area in each product is characterized in thatNumber of (2)Then, thenThe formula for calculating the coordination coefficient is as follows:and obtaining a coordination coefficient between different parent nodes according to the same method, wherein the coordination coefficient is used as the edge weight of the two nodes.

So far, each node in the topological graph data is obtained, each node represents a style characteristic subclass, and the score of the subclassIs the signal value of the node; the edge weight between each node is a coordination coefficient of two nodes; it should be noted that there is no edge right between two nodes belonging to the same parent class.

Constructing an objective function, and obtaining an optimal style combination from topological graph data:

wherein， Is a combination of the styles of the three regions,the weight of the edge in between,the signal value of (a). The result of the great optimization by the gradient descent method is the optimal style combination which respectively corresponds to the style of the background area, the style of the font area and the style of the pattern area, so that the visual effect is optimal and the harmony is optimal.

Step (ii) of: according to the medicineBest distribution map ofThe optimal style combination and the content of the style combination result in the final design result.

Thus obtaining the medicineBest distribution map ofThe best style combination, the content of the medicine is given by the designer and the medicineThe efficacy and the enterprise culture are related. According to the optimal distribution mapAnd design content is availableThe content information of the corresponding area.

Deriving the style of the background region from the optimal style combinationStyle of font regionAnd the style of the pattern regionAccording to the content information of each region and the style characteristics of each region, the design result of the corresponding region is obtained by using a style migration method, it should be noted that the style migration method is the prior art and is not within the protection scope of the present invention, and is not described herein again. It should be noted that the content information of the background area and the style of the background area are usedObtaining a final design result layer of the background area; and obtaining final design result image layers of the three areas according to the same method, and superposing the three image layers to obtain a final medicine package design result.

The above embodiments are merely illustrative of the present invention, and should not be construed as limiting the scope of the present invention, and all designs identical or similar to the present invention are within the scope of the present invention.